Research on Virtual Try-On Based on Deep Learning

This paper innovatively proposes a virtual try-on network that integrates an attention mechanism and appearance flow to address the issues of poor try-on between clothing and the target human body, as well as unnatural deformation, in existing virtual try-on systems. Specifically, a unique information exchange module is designed, which successfully integrates the clothing deformation and segmentation generation steps, significantly enhancing the information interoperability. And a network architecture based on the appearance flow is used to generate deformed clothing and semantic segmentation maps and perform refined processing on them. In addition, a Multi-scale Dilated Fusion attention mechanism is carefully designed and introduced. This mechanism can efficiently capture multi-scale information while expanding the receptive field, which is extremely beneficial for obtaining accurate target segmentation maps and deformed clothing. Aiming at the disadvantage of excessive local deformation that may be caused by the highly free appearance flow, regularization processing methods and flow alignment operations are adopted to optimize and obtain naturally deformed clothing. Experiments on existing benchmark datasets fully confirm that the method proposed in this paper can more effectively retain the texture details of clothing and further generate clothing images with more natural deformation effects, providing a strong technical support for the development of virtual try-on technology.